Application of machine learning to analyze academic performance of university students

Aleksey V. Alpatov

Volzhsky polytechnic institute (branch) of Volgograd state technical university

The paper presents the results of analyzing and predicting the educational results of first-year university students in the implementation of a separate discipline using machine learning. The relevance of the research topic is due to the need for universities in modern conditions to successfully compete in the educational services market, which is characterized by a low number of applicants and an increase in requirements for the quality of vocational education both on the part of applicants and on the part of the state. An important component for effective decision-making in the process of quality management of the educational process is educational analytics, on the basis of which it is possible to predict the academic performance of students, to identify factors that have a significant impact on achieving high educational results. The study showed the possibility of predicting the exam in a particular discipline of first-year university students based on the data of control sections conducted by deans during the semester to identify groups of students with an increased risk of academic debt. The prediction accuracy shown by the constructed models (neural network, decision tree and logistic regression) turned out to be quite acceptable both at the stage of the first boundary control and at the stage of the second. The results of this work are of practical importance for the administration of universities and for teachers. Predictive models can be used to predict the expulsion of students due to academic failure. Models can be embedded in educational information systems and be an assistant to teachers for decision-making in the process of implementing the discipline.

students’ performance prediction, learning analytics, educational data mining, unified state exam, neural networks, decision tree, logistic regression, clustering